We describe a technique for comparing distributions without
the need for density estimation as an intermediate step. Our approach relies on
mapping the distributions into a Reproducing Kernel Hilbert Space. We apply
this technique to construct a two-sample test, which is used for
determining whether two sets of observations arise from the same
distribution. We use this test in attribute matching for
databases using the Hungarian marriage method, where it performs strongly.
We also demonstrate excellent
performance when comparing distributions over graphs, for which no
alternative tests currently exist.